Performance Debugging & Benchmarking
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Inference Performance Debugging Interview Questions
p99 TTFT regressions with flat utilisation, OOMs under continuous batching, numerical divergence after an engine upgrade, nondeterministic outputs, slow replicas, and benchmark methodology that does not lie about warm-up, clocks or prompt mix.
Worked answers on inference mechanisms, with calculations, implementation details, and follow-up questions.
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01–09Foundationsthe physics and terms an interviewer will not stop to define0/9 done
10–17Core loopsthe standard questions of this round, worked end to end0/8 done
18–21Field scenariosproduction incidents and capacity problems with the inputs left incomplete0/4 done
The concepts behind Performance Debugging & Benchmarking
The ideas these answers lean on. Foundation concepts are free to read; the advanced ones are Premium.
Foundational
Latency Decomposition: Queue, Prefill, DecodeHow to break LLM request latency into its parts: TTFT is network plus queue plus prefill, end-to-end adds output length times inter-token latency. Which vLLM metrics measure each part, why percentiles do not add, and how Little's law cross-checks a dashboard.Advanced
Inference Benchmarks That Do Not LieAnalyze scheduled-arrival and send timestamps, account for failures, and reproduce goodput from a small request trace. Then use an executable queue model to see what a closed-loop client hides before designing a matched-load benchmark.🔒 Premium
Advanced
Nondeterminism and Batch InvarianceWhy an LLM endpoint gives different outputs at temperature 0: floating-point addition is not associative, kernels pick their reduction strategy (split-K, split reductions, split-KV) from the batch shape, and server load sets the batch. What atomics do and do not explain, what batch-invariant kernels change, and what they cost.🔒 Premium
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